Fırçasız DC Motorun Denge Optimizasyon Algoritması Tabanlı FOPID Kontrolü

Öz

PID kontrolün ana zorlukları, zayıf yanıta yol açan ani ayar noktası değişiklikleri ve parametre değişiklikleridir. Bu kontrol ünitesinin FOPID olarak bilinen başka bir benzer kontrol ünitesi ile değiştirilebileceği dikkate alınabilmektedir. Ancak entegrasyon ve farklılaşma derecesinde ondan farklıdır ve bu, geçici durumda sistemin performansını artırmaktadır. FOPID katsayılarını seçmek için, mümkün olan en iyi performansı elde etmek için optimizasyon algoritmaları da dahil olmak üzere çeşitli metodolojiler kullanılmaktadır. Bu makalede fırçasız DC motor (BLDC) hız kontrolü FOPID yapılmıştır. Kontrolör parametrelerini belirlemek için EO algoritması uygulanmıştır ve bu algoritmanın performansı, PSO, DE ve GJO gibi diğer optimizasyon algoritmalarıyla karşılaştırılmıştır. Matlab-Simulink 2016a'daki simülasyon sonuçları, önerilen algoritmanın (EO) diğer algoritmalara kıyasla daha iyi tepki süresi, aşım ve daha düşük kararlı hal hatası elde etmedeki etkinliğini göstermektedir.

Anahtar Kelimeler

• Equilibrium Optimizer
• FOPID
• PID
• BLDC motor.

Equilibrium Optimizer Based FOPID Control of BLDC Motor

Abstract

The main challenges of proportional integral derivative (PID) control are sudden set-point changes and parameter changes, which leads to poor response. It can be taken into account that this control unit can be replaced by another similar control unit, but it differs from it in the degree of integration and differentiation, and this is what is known as fractional-order PID (FOPID), which improves the performance of the system in the transient state. To choose the FOPID constants, various methodologies, including optimization algorithms, are used to obtain the best possible performance. In this paper, the speed of brushless DC motor (BLDC) was regulated using (FOPID), where the equilibrium optimizer (EO) algorithm was used to find the values of the controller constants, and the performance of this algorithm was compared with several other optimization algorithms such as particle swarm optimization (PSO), differential evolution (DE), and golden jackal optimization (GJO). Simulation results in Matlab-Simulink 2016a showed the effectiveness of the proposed algorithm (EO) in achieving response time, overshot, and lower steady state error compared with the rest of the algorithms.

Keywords

• Equilibrium Optimizer
• FOPID
• PID
• BLDC motor

Kaynakça

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